ServiceNow and NVIDIA Announce Partnership to Build Generative AI Across Enterprise IT

Generative Workflow

ServiceNow and NVIDIA Announce Partnership to Build Generative AI Across Enterprise IT

Built on ServiceNow Platform With NVIDIA AI Software and DGX Infrastructure, Custom Large Language Models to Bring Intelligent Workflow Automation to Enterprises

May 17, 2023

ServiceNow and NVIDIA Announce Partnership to Build Generative AI Across Enterprise IT

Knowledge 2023—ServiceNow and NVIDIA today announced a partnership to develop powerful, enterprise-grade generative AI capabilities that can transform business processes with faster, more intelligent workflow automation.

Using NVIDIA software, services and accelerated infrastructure, ServiceNow is developing custom large language models trained on data specifically for its ServiceNow Platform, the intelligent platform for end-to-end digital transformation. 

This will expand ServiceNow’s already extensive AI functionality with new uses for generative AI across the enterprise — including for IT departments, customer service teams, employees and developers — to strengthen workflow automation and rapidly increase productivity. 

ServiceNow is also helping NVIDIA streamline its IT operations with these generative AI tools, using NVIDIA data to customize NVIDIA® NeMo™ foundation models running on hybrid-cloud infrastructure consisting of NVIDIA DGX™ Cloud and on-premises NVIDIA DGX SuperPOD™ AI supercomputers.

“IT is the nervous system of every modern enterprise in every industry,” said Jensen Huang, founder and CEO of NVIDIA. “Our collaboration to build super-specialized generative AI for enterprises will boost the capability and productivity of IT professionals worldwide using the ServiceNow platform.”

“As adoption of generative AI continues to accelerate, organizations are turning to trusted vendors with battle-tested, secure AI capabilities to boost productivity, gain a competitive edge, and keep data and IP secure,” said CJ Desai, president and chief operating officer of ServiceNow. “Together, NVIDIA and ServiceNow will help drive new levels of automation to fuel productivity and maximize business impact." 

Harnessing Generative AI to Reshape Digital Business 

ServiceNow and NVIDIA are exploring a number of generative AI use cases to simplify and improve productivity across the enterprise by providing high accuracy and higher value in IT. 

This includes developing intelligent virtual assistants and agents to help quickly resolve a broad range of user questions and support requests with purpose-built AI chatbots that use large language models and focus on defined IT tasks. 

To simplify the user experience, enterprises can customize chatbots with proprietary data to create a central generative AI resource that stays on topic while resolving many different requests.

These generative AI use cases are also applicable to customer service agents, allowing for case prioritization with greater accuracy, saving time and improving outcomes. Customer service teams can use generative AI for automatic issue resolution, knowledge-base article generation based on customer case summaries, and chat summarization for faster hand-off, resolution and wrap-up. 

In addition, generative AI can improve the employee experience by helping identify growth opportunities. For example, delivering customized learning and development recommendations, like courses and mentors, based on natural language queries and information from an employee’s profile. 

Full-Stack NVIDIA Generative AI Software and Infrastructure Fuel Rapid Development

In its generative AI research and development, ServiceNow is using NVIDIA AI Foundations cloud services and the NVIDIA AI Enterprise software platform, which includes the NVIDIA NeMo framework. 

Included in NeMo are prompt tuning, supervised fine-tuning and knowledge retrieval tools to help developers build, customize and deploy language models for enterprise use cases. NeMo Guardrails software is also included and enables developers to easily add topical, safety and security features for AI chatbots.

How Generative AI is Changing the Knowledge Paradigm for Enterprises

Good thoughts

From VB Spotlight

How generative AI is changing the knowledge paradigm for enterprises

VB Staff, May 12, 2023 5:05 AM

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Learn More   Presented by Glean

At the enterprise level, keeping track of internal data and information has become an enormous challenge. In this VB Spotlight event, learn how new generative AI experiences are unlocking the full potential of data in enterprise environments and reducing time to knowledge.

With the increasing complexity and distributed nature of organizations – far-flung teams, remote work, and a multitude of knowledge systems, data is difficult to track down across an entire enterprise knowledge ecosystem, and workers are feeling the toll.

Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.

This knowledge access challenge “results in a loss of productivity and a frustration that we’re starting to see, leading to diminishing engagement from our employees,” says Phu Nguyen, head of digital workplace at Pure Storage during the recent VB Spotlight, “The impact of generative AI on enterprise search: A game-changer for businesses.

He was joined by Jean-Claude Monney, a digital workplace, technology and knowledge management advisor and Eddie Zhou, founding engineer, intelligence at Glean to discuss the emergence of the evolutionary leap forward in workplace-specific search tools, powered by generative AI, that gives employees full access to the knowledge they need, and its context, anywhere in the organization.

The evolution of enterprise search

Traditional enterprise search can’t reach all the knowledge in an organization, which is spread out in multiple systems. It can mine structured knowledge, such as the data found in Jira, Confluence, intranets and sales portals, but unstructured knowledge, the information communicated through IM, Teams, Slack, and email, has been uncharted territory, difficult to corral in any helpful contextual way, Nguyen adds.

“The paradigm of knowledge management has changed significantly,” he says. “How do you have a system that can look at both structured and unstructured data and provide you with the answers that you’re ultimately looking for? Not the information that you need, but the answer that you’re looking for.”

Solutions that integrate with multiple systems and utilize generative AI can address these challenges, and help employees find the information they need to perform their jobs effectively, no matter where that knowledge resides.

“Companies are now building searches specifically for the workplace, built for internal searches that work across your internal system,” Nguyen explains. “Most importantly, they’re built on a knowledge graph that returns a search that’s more relevant to your employees. This is all very exciting for us because we think of this as part of our employee information center strategy. Previously it was just an intranet and our support portal, but now we have this workplace search that can connect information across multiple systems inside our organization.”  ... ' 

Emerging AI Attached to Business Meetings and Process Management.

Some of the work we did in the enterprise a dozen plus years ago was to improve executive decision making based on visualized data.   Especially useful when it needed to focus complex, expensive  and difficult to obtain expertise.    And results that further need to be linked to known and developing process.  At the time we were also working with strategic AI approaches, but it had yet to mature.   It comes to mind now that methods like GPT make this easier, especially when integrated with data visualization and large data base exploration.     Exploring further .... 

Here is a set of posts:   from this blog about the work then.  

Connect to discuss ...     Franz   

Defending Your Enterprise

Modeling your Defense with Chaos.

Defending the Enterprise

By David Geer, July 26, 2022 

Cyberattacks bring turbulence and disruption, leaving unpredictable and chaotic system failures in their wake. Organizations using cybersecurity chaos experiments simulate cyber events to uncover deficits in their cyber-elasticity. Test results lead developers and engineers to repair or rearchitect applications and supporting infrastructure for security and continuity under trial.

In the context of cybersecurity, chaos is any security failure that can happen, says Kelly Shortridge, senior principal product technologist, Fastly, an edge cloud platform provider. "Security chaos testing is the practice of continual experimentation to verify that systems operate as we think to improve their resilience to attack," explains Shortridge.

"Cybersecurity chaos engineering is resiliency testing adopted to combat the ever-changing threat landscape. Chaos engineering applies new threats to systems to see what happens to an ecosystem if components fail," says Doug Saylors, partner and co-lead, cybersecurity, for ISG, a global technology research and advisory firm. "Running a penetration test or attack simulation with a zero-day exploit is the most common method of chaos testing in cybersecurity," notes Saylors.

Web applications, distributed systems, and network infrastructure, including infrastructure-as-code, break under the pressure of attacks that bring chaos. "Systems have varying response characteristics depending on the type of attack. Applications stop working or provide erroneous outputs that have downstream effects. Network and infrastructure components see significant performance spikes, which affect users negatively through increased latency or limited access to critical applications," says Saylors.

Criminal hackers are willing to cause chaos to get to the underlying data, says Jenn Bergstrom, senior technical director for Parsons X; they hope you are not monitoring closely enough to quickly notice the signs, such as packet loss. Parsons X is a group within Parsons, a digitally-enabled solutions provider. "Packet loss may happen because they send a jumbo packet with an embedded command that will affect your database. So, the chaos is more of a side effect of what they are doing," says Bergstrom.

"It's important to see how your system behaves" under such circumstances, "so you see those minor differences between standard behavior and the unexpected," says Bergstrom.

Specific attacks create lots of chaos. "The main attack space for security chaos is ransomware, even though its goal is to make money," says Shortridge. However, ransomware causes more security failures than encrypting critical data. It locks enterprises out of systems and machines, and leads to downtime until an organization pays the ransom or restores from backups. 

Ransomware attacks are resource-intensive, requiring network bandwidth, CPU cycles, and hard drive operations to encrypt many files quickly, completing an attack before the organization has time to stop it. Ransomware can take down entire networks. It can encrypt all backups before proceeding to production data to ensure the organization cannot restore it. Any services that count on that data come to a halt. Any software with dependencies on those services suffers, and those operations cease or falter. Cybersecurity chaos experiments must evolve to meet the challenges of modern ransomware. ... ' 

Getting Serious about Data Science

Fairly obvious, seen many of these situations occur over the years in analytics and data interactions.  And how they are linked to business results.   Good checklist.   Non technical.

Getting Serious About Data and Data Science  To implement successful data programs, companies need to shift goals, muster resources, and align people.

By Thomas C. Redman and Thomas H. Davenport 

Data science, including analytics, big data, and artificial intelligence, is no longer a novel concept. Nor is the important foundation of high-quality data. Both have contributed to impressive business successes — particularly among digital natives — yet overall progress among established companies has been painfully slow. Not only is the failure rate high, but companies have also proved unable to leverage successes in one part of the business to reap benefits in other areas. Too often, progress depends on a single leader, and it slows dramatically or reverses when that individual departs the company. In addition, companies are not seizing the strategic potential in their data. We’d estimate that less than 5% of companies use their data and data science to gain an effective competitive edge. ... " 

Gartner Hype Cycle on AI

Nicely done overview, including the classic hype cycle of AI at the link.

Hype Cycle of AI In The Enterprise AI    By Svetlana Sicular  

We recently published for the wide audience that 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020.  Two megatrends – industrialization of AI platforms and democratization of AI – indicate that production workloads and high-scale AI applications are looming in the near future.  This means that AI will be reaching significantly more people via democratization of AI, and it requires industrialized platforms that accelerate and automate the AI development and implementations process to make AI accessible to the masses.

Let’s take a deeper look at industrialization of AI on the Hype Cycle for Artificial Intelligence, 2020. The industrialization of AI platforms enables reusability, scalability and safety, which accelerate AI adoption and growth. If early AI adopters were mostly a grassroots and bottom up movement, the current AI wave is top-down. The C-suite are leading the charge in initiating AI projects now, with nearly 30% of the projects directed by CEOs. These projects aim to swiftly deliver value to the enterprise and catch up with the early adopters. That’s why the Machine Learning profile has already crossed into the Trough of Disillusionment: Simply mastering ML is not enough. The current wave expects AI tools to be on par with the enterprise production requirements and known processes, such as convenient AI development environments, automation of routine tasks, production stability and reliability.  .... 

(Hype Cycle of AI Here) 

Moving to Enterprise Tech Beyond Survival Mode

Enterprise Tech Efforts Move Beyond Survival Mode

The Wall Street Journal , Angus Loten

Many companies that rushed to install remote-work applications at the start of the pandemic now are moving forward with their pre-Covid-19 technology plans. Enterprise technology firms have seen more companies act on their plans to expand use of cloud computing, data analytics, smart software, and other technologies as lockdowns end. In a recent analysis by technology research firm Gartner, the impact of Covid-19 was cited by almost 70% of corporate boards for increased spending on IT and digital capabilities. Similarly, in a survey of 800 global business executives by consulting firm McKinsey & Co., about half said the pandemic has prompted their firms to accelerate the implementation of new technologies.  ... " 

Uncertain Future of Corporate HQs

Questionable, though major HQs will likely still need the presence.   My guess is that many will be threatened. 

The Uncertain Future of Corporate HQs    in HBR by Richard Florida

The Covid-19 pandemic has seen tens of millions of Americans engage in a gigantic experiment in working from home — one that looks to be more permanent than anyone might have imagined. Corporation after corporation has announced that they won’t be reopening their offices until mid-2021, at least.  Some commentators are even predicting the death of the office and the end of cities.

But let’s not get too far ahead of ourselves. Now, more than ever, the issue of where we work — of place and location — remains a fundamental question.

Pandemics and other crises can disrupt or change the status quo, but history shows they can also accelerate trends already underway. The question of where to locate corporate facilities has been increasing in strategic importance for a long time. Corporations were facing a rising backlash to their perceived effects on housing prices and gentrification in superstar cities and tech hubs, and from attempts to hoard taxpayer-financed incentives   ....   '

SAP and IBM Announce New Intelligence Offerings

Next steps between SAP and IBM partnership:  Digital Transformation towards the intelligent enterprise.

In Cision: PRNewswire  https://www.prnewswire.com/

IBM and SAP Announce New Offerings to Help Companies' Journey to the Intelligent Enterprise
ARMONK, N.Y. and WALLDORF, Germany, June 23, 2020 /PRNewswire/ -- IBM (NYSE: IBM) and SAP SE (NYSE: SAP) today announced their partnership's next evolution, with plans to develop several new offerings designed to create a more predictable journey for businesses to become data-driven intelligent enterprises. Over 400 businesses have modernized their enterprise systems and business processes through IBM and SAP's digital transformation partnership. 

Nokia Research on 5G

Indications of the importance of emerging 5G:

In Global newswire:
SomeindNew Nokia research reveals biggest 5G drivers for enterprise IT and OT

Press Release New Nokia research reveals biggest 5G drivers for enterprise IT and OT Survey finds 65% of IT decision-makers are aware of 5G, one-third are using it today and 47% have started their 5G planning Businesses identify video as most .... '

Anaconda and IBM Watson Team to Simplify Enterprise AI

This was needed to provide easier to use capabilities in the enterprise.

Anaconda and IBM Watson Team to Simplify Enterprise Adoption of AI Open-Source Technologies
Anaconda, Inc.​, provider of the leading Python data science platform, and IBM Watson (NYSE: ​IBM​) today announced a new collaboration to help simplify enterprise adoption of AI open-source technologies.  ....

- Anaconda will feature IBM’s Watson Studio no-charge plan to its community of 20 million data scientists

AUSTIN, Texas, and ARMONK, New York, June 04, 2020 (GLOBE NEWSWIRE) -- Anaconda, Inc.​, provider of the leading Python data science platform, and IBM Watson (NYSE: ​IBM​) today announced a new collaboration to help simplify enterprise adoption of AI open-source technologies. By working together, the two companies plan to help fuel innovation and address the AI and data science skills gap that many enterprises face today.

Today, we see that data scientists rely on open-source technologies for innovation and to tap into a wealth of AI skills and talent. However, using open source in the enterprise could be complex to adopt and manage from both a technical and operational standpoint.

To help enterprises overcome this challenge, Anaconda Team Edition repository will be integrated with IBM Watson Studio on IBM Cloud Pak for Data, enabling organizations to better govern and speed the deployment of AI open-source technologies across any cloud. Additionally, Anaconda’s community of 20 million users will now be able to access a no-charge lite plan of Watson Studio, offering the opportunity to manage their data science projects within IBM’s enterprise-grade environment. ..... '

SAS on How AI Changes the Rules

Always liked SAS as a analytical problem solving company, here a new paper from them of interest, register for it at the link.

How AI Changes the Rules
New Imperatives for the Intelligent Organization

About this paper
Many leaders are excited about AI’s potential to profoundly transform organizations by making them more innovative and productive. But implementing AI will also lead to significant changes in how organizations are managed, according to our recent survey of more than 2,200 business leaders, managers and key contributors. Those survey respondents, representing organizations across the globe, expect that reaping the benefits of AI will require changes in workplace structures, technology strategies and technology governance.to manage the significant changes to software development and deployment processes that most respondents expect from AI.

AI will drive organizational change and ask more of top leaders. The majority of survey respondents expect that implementing AI will require more significant organizational change than other emerging technologies including cloud. AI demands more collaboration among people skilled in data management, data analytics, IT infrastructure, and systems development, as well as business and operational experts. This means that organizational leaders need to ensure that traditional silos don’t hinder advanced analytics efforts, and they must support the training required to build skills across their workforces.

AI will place new demands on the CIO and CTO. AI implementation will influence the choices CIOs and CTOs make in setting their broad technology agendas. They will need to prioritize developing foundational technology capabilities, from infrastructure and cybersecurity to data management and development processes — areas in which those with more advanced AI implementations are taking the lead compared with other respondents. CIOs will also need to manage the significant changes to software development and deployment processes that most respondents expect from AI. The survey also indicated that many CIOs will be charged with overseeing or supporting formal data governance efforts: CIOs and CTOs are more likely than other executives to be tasked with this.

AI will require an increased focus on risk management and ethics. The Global survey shows a broad awareness of the risks inherent in using AI, but few practitioners have taken action to create policies and processes to manage risks, including ethical, legal, reputational, and financial risks. Managing ethical risk is a particular area of opportunity. Those with more advanced AI practices are establishing processes and policies for data governance and risk management, including providing ways to explain how their algorithms deliver results. They point out that understanding how AI systems reach their conclusions is both an emerging best practice and a necessity, in order to ensure that the human intelligence that feeds and nurtures AI systems keeps pace with the machines’ advancements.

The report that follows explores these findings in depth. Read on to learn more about the changes that leaders must prepare for to successfully implement trusted AI.   ... "

Waves Enterprise Blockchain

Been examining a number of applications of smart contracts recently, including those more closely connected to specific goal oriented and regulatory agreements and tasks.  With strong identity and process security.   Here another example.  See my 'smart contracts' tag below for more examples.

Waves Enterprise blockchain unveils major updates and hundreds of smart contracts per minute  By Kyt Dotson in SiliconAngle

Fast-transaction blockchain distributed ledger provider Waves Enterprise, an extension of Waves Platform AG technology, Thursday announced major upgrades to its network that adds significant improvements, putting it into the same class as the corporate blockchain Hyberledger Fabric.

Waves Enterprise, now in full Version 1.0, offers what the company says a powerful universal blockchain solution aimed at corporations and the public sector. Key features added in this update include containerized smart contracts, greatly improved performance, an updated application programming interface for developers and an improved user interface for users.

“There is now a transition to a new generation of IT systems and a new system of interaction between companies and even people,” said Alexander Ivanov, founder of Waves. “We are talking about the interaction between companies, which can be transferred to new tracks. This became possible with the advent of blockchain. This technology is designed to shape ecosystems in which participants trust each other.”

This is a major release for Waves Enterprise and deploys features that implement important new functions for enterprise clients. To start, Waves Enterprise has implemented an authorization service that will adhere to enterprise-level security using the oAuth 2.0 specification. Previously the Waves API was open, thus accessible publicly and available for anyone.   ... "

State of AI in the Enterprise

Useful view from recent surveys Deloitte in 2017 and 2018 by Deloitte.

Irving Wladawsky-Berger reports on The State of AI in the Enterprise

A few months ago, Babson College professor Tom Davenport gave a talk on the state of AI in the enterprise at the annual conference of MIT’s Initiative on the Digital Economy.  His talk was based on two recent US surveys conducted by Deloitte, the first one in 2017 followed by a second in 2018.  Davenport was a co-author of both reports.

The 2017 survey was focused on the responses of 250 US executives who were leading the applications of AI in their companies.  The larger 2018 survey reached out to 1,100 IT (46%) and line-of-business (54%) executives from US-based companies (64% at the C-level) and 10 different industries.  All of these respondents were early AI adopters compared with their counterparts in an average company, - 90% were directly involved in their company’s AI projects, and 75% said that they had an excellent understanding of AI.

Davenport started his talk by summarizing the key findings in the Deloitte surveys:

20-30% of enterprises are early adopters, having implemented at least one AI prototype or production application;

Many projects are in pilots but some are already in production;
Relatively simple low hanging fruit projects prevail over more ambitious and complex moon shots;
Only 24% cited “reducing headcount through automation” as one of their top AI priorities;
The great majority of respondents believe that AI leads to moderate or substantial changes in job roles and skills;

Implementation, integration, data issues and talent top the list of challenges faced by early adopters;
Further AI growth is inevitable.

Overall, the 2018 survey found that “Early adopters are ramping up their AI investments, launching more initiatives, and getting positive returns.”  Compared to executives in average companies, early adopters have been implementing key AI technologies at a growing rate, including machine and deep learning, natural language processing and computer vision.  63% of respondents had adopted machine learning, an increase of 5% over the 2017 survey and 50% were using deep learning.  62% had adopted natural language processing, compared to 53% in 2017, while 57% were using computer vision.  .... "

Data as Valued Corporate Asset

A topic I have now followed for some time, we even implemented aspects of valuation in the enterprise by starting with semantic structures.    Good thoughts at the link.  Value is meta data of the most important kind,  but also slippery in its use and maintenance.  Can we express it in a kind of value chain?    Linked to a market?    Just now talking the topic again seriously.

What is Data Value and Should it be Viewed as a Corporate Asset?
By Asha Saxena  in Dataversity

A lot has changed since the 1950’s when the average age of a publicly traded company listed on the S&P 500 has gone down from being 60 years old to now less than 20 years old according to research from Credit Suisse investor analysts. Companies that are embracing using new technologies, automation, Big Data, Machine Learning, and innovation are gaining market share on the list, and are disrupting older legacy businesses that have been slower to adapting and transforming to digital changes. In fact 5 of the 6 biggest companies  (Apple, Amazon, Alphabet, Microsoft, Facebook) by market cap valuation are data technology businesses. Companies that understand the true value of their data and leverage it with advanced analytics technologies are seeing continued growth. A Forbes contributor Howard Baldwin makes a comparison to prove the point of proving data’s value:

“Why is Facebook currently valued at 415 billion and United Airlines, a company that actually owns things like airplanes and has licenses to lucrative things like airport facilities and transoceanic routes between the U.S. and Asia, among other places, is only worth $24 billion.”

These disruptive innovators, use Big Data solutions as competitive advantages to reduce operational costs, increase revenue, predict behavior, improve cash flows. They weave data into every function of the organization. Data is not only being used to record what has transpired, but it also being used to predict and drive transformative disruptive changes at alarming speeds. And yet how many companies are listing their data as tangible corporate asset on their balance sheet?  ... " 

Avoiding Blockchain Mistakes

Nicely done set of cautions, including strong mention of 'smart contracts'.  Have passed this to colleagues.

7 blockchain mistakes and how to avoid them
The blockchain industry is still something of a wild west, with many cloud service offerings and a large universe of platforms that can vary greatly in their capabilities. So enterprises should beware jumping to conclusions about the technology.   .... "
           
By Lucas Mearian in Compterworld  .... 

Originating Gartner Press Release  .... 

Wireless Networking for Everywhere

What are the implications for new infrastructure of Wifi Networking Everywhere?

Enterprise Networks in Cisco Blog
Next Generation Wireless Infrastructure for Intent-based Networking Everywhere   By Anand Oswal

We’ve come to expect that our mobile devices are always connected to our favorite applications and data sources via Wi-Fi and LTE. As enterprises move more resources to multiple domains—data center, campus, branch edge, and mobile cellular—always-available wireless connectivity for devices is not just a convenience, it’s essential for keeping business operations running. With many business processes dependent on cloud data storage and processing, there is no tolerance for network latency, congestion, or down-time from maintenance upgrades. Waiting for applications to respond because of overloaded Access Points (APs) hinders employee productivity and degrades customers’ experience. The explosive growth of IoT devices is adding yet another layer of complexity for ubiquitous Wi-Fi connectivity at a scale that will only grow over time.

To meet the demand for always-available connectivity for business and personal applications—especially those using rich, visual content—organizations will rely on the new Wi-Fi 6 standard for campus and intra-branch connectivity and LTE/5G for mobile and field connections to enterprise resources. The new technologies and improvements in Wi-Fi 6 are one reason we re-engineered Cisco Aironet Access Points, creating the new Catalyst 9100ax AP and the Catalyst 9800 series wireless controllers with augmented Wi-Fi 6 capabilities to provide always-available wireless connectivity coupled with always on-guard security for a multi-domain world of data and applications. ... " 

SAS and AI

More on this.was a long time user of SAS in the enterprise, so the connection is interesting.

SAS Lays Out AI, Machine Learning Enhancements

The data science and machine learning platform market is much more competitive today than it was 10 years ago. Here's how SAS plans to compete.

When it comes to data analytics in the enterprise, SAS has pretty much always been among the top players. Its customers are in the big league, too: Bank of America, Honda, and Nestle, for instance. SAS claims that it works with 94% of Fortune 100 companies in some capacity. Gartner classifies SAS as a "Leader" in data science and machine learning platforms.

Machine learning and artificial intelligence technologies are among the hottest identified by top companies for investment over the next several years. Gartner's 2018 CIO survey showed that only 4% of CIOs have implemented AI, but that 46% have developed plans to implement it. It's an environment that would seem like it should be driving a gold rush for SAS. Yet it is not without its challenges.  .... "

Companies Take Piecemeal Approach to Automation Tech

Fairly obvious.   Has been the case with every emerging tech since computing emerged.  We saw it with early analytics.   You test at small scales.  To understand results versus goal, consequences known and unintended.  And deployment costs.  Every test also includes inserting results into existing process context, which takes time and requires result measurements.    Stats here are useful.

Companies Take a Piecemeal Approach to Automation Tech 
The Wall Street Journal   By Angus Loten

KPMG surveyed about 600 C-suite executives to determine how companies are approaching the deployment of automation technologies. About 30% of respondents said their companies have apportioned $50 million or more into smart automation projects, and more than 50% have already spent at least $10 million. Such projects include diverse combinations of robotic process automation, artificial intelligence, machine learning, cognitive computing, and analytics; thus far, funding is going into corporate finance and accounting functions, followed by group benefits strategies, compliance, and industry-specific core operations. More than 50% of respondents listed improving or streamlining customer services and front-office effectiveness as their primary goal. Many companies have adopted a piecemeal strategy to automation, due to uncertainty about how much investment will be needed to make deployments worthwhile, as well as a dearth of "organizational clarity and accountability."  ... '

Future of the Firm

In O'Reilly, about the firm, its future, and who is doing the work.  Today often part-time.  A considerable piece with lots of links to related work.

Future of the firm
Mapping the complex forces that are reshaping organizations and changing the employee/employer relationship.

By Josh Bersin, Tim O'Reilly, Roger Magoulas, Mike Loukides:  March 12, 2019

Future of the Firm

We’re excited that O’Reilly Radar is partnering with Josh Bersin to cover topics like organizational learning, workforce expectations, adapting to digital transformation, and leveraging technology for human resources—all topics related to the future of the firm. Bersin has a long history of providing prescient advice on the human side of what organizations need to tackle the changing technological landscape they face. You may know him from the research reports he developed at Bersin by Deloitte.

A downloadable edition of "Future of the Firm" is also available.

The “future of the firm” is a big deal. As jobs become more automated, and people more often work in teams, with work increasingly done on a contingent and contract basis, you have to ask: “What does a firm really do?” Yes, successful businesses are increasingly digital and technologically astute. But how do they attract and manage people in a world where two billion people work part-time? How do they develop their workforce when automation is advancing at light speed? And how do they attract customers and full-time employees when competition is high and trust is at an all-time low?

When thinking about the big-picture items affecting the future of the firm, we identified several topics that we discuss in detail in this report: ... "